Whole slide microscopic slides display many cues about the underlying tissue guiding diagnostic and the choice of therapy for many diseases. However, their enormous size often in gigapixels hampers the use of traditional neural network architectures. To tackle this issue, multiple instance learning (MIL) classifies bags of patches instead of whole slide images. Most MIL strategies consider that patches are independent and identically distributed. Our approach presents a paradigm shift through the integration of spatial information of patches with a sparse-input convolutional-based MIL strategy. The formulated framework is generic, flexible, scalable and is the first to introduce contextual dependencies between decisions taken at the patch level. It achieved state-of-the-art performance in pan-cancer subtype classification. The code of this work will be made available.

中文翻译：

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用于整个幻灯片图像分类的稀疏卷积上下文感知多实例学习

整个载玻片显微镜载玻片显示出许多有关潜在组织的线索，可指导诊断和多种疾病的治疗选择。但是，它们巨大的尺寸（通常以千兆像素为单位）阻碍了传统神经网络体系结构的使用。为了解决此问题，多实例学习（MIL）对补丁包进行分类，而不是对整个幻灯片图像进行分类。大多数MIL策略都认为补丁是独立的并且分布均匀。我们的方法通过将补丁的空间信息与基于稀疏输入的基于卷积的MIL策略进行集成，提出了一种范式转换。制定的框架具有通用性，灵活性，可扩展性，并且是第一个在补丁程序级别引入的决策之间引入上下文相关性的框架。它在全癌亚型分类中取得了最先进的性能。